Generalized Pseudospectral Shattering and Inverse-Free Matrix Pencil Diagonalization
arxiv(2023)
摘要
We present a randomized, inverse-free algorithm for producing an approximate
diagonalization of any n × n matrix pencil (A,B). The bulk of the
algorithm rests on a randomized divide-and-conquer eigensolver for the
generalized eigenvalue problem originally proposed by Ballard, Demmel, and
Dumitriu [Technical Report 2010]. We demonstrate that this divide-and-conquer
approach can be formulated to succeed with high probability provided the input
pencil is sufficiently well-behaved, which is accomplished by generalizing the
recent pseudospectral shattering work of Banks, Garza-Vargas, Kulkarni, and
Srivastava [Foundations of Computational Mathematics 2022]. In particular, we
show that perturbing and scaling (A,B) regularizes its pseudospectra,
allowing divide-and-conquer to run over a simple random grid and in turn
producing an accurate diagonalization of (A,B) in the backward error sense.
The main result of the paper states the existence of a randomized algorithm
that with high probability (and in exact arithmetic) produces invertible S,T
and diagonal D such that ||A - SDT^-1||_2 ≤ε and ||B -
ST^-1||_2 ≤ε in at most O (log^2 (
n/ε) T_MM(n) ) operations, where
T_MM(n) is the asymptotic complexity of matrix multiplication. This
not only provides a new set of guarantees for highly parallel generalized
eigenvalue solvers but also establishes nearly matrix multiplication time as an
upper bound on the complexity of inverse-free, exact arithmetic matrix pencil
diagonalization.
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